Word embedding techniques, representing each discrete word as a dense vector in continuous high-dimension space, have achieved successes in many natural language processing (NLP) tasks. However, more NLP tasks rely on modelling variable-length sequences of words, not just isolated words. The conventional approach is to formulate these NLP tasks as sequence labelling problems and apply conditional random fields (CRF), convolutional neural networks (CNN) and recurrent neural networks (RNN). In this talk, I will introduce a new general deep learning approach applicable to almost all NLP tasks, not limited to sequence labelling problems. The proposed method is built upon a simple but theoretically-guaranteed lossless encoding method, namely fixed-size ordinally-forgetting encoding (FOFE), which can almost uniquely encode any variable-length word sequence into fixed-size representation [1]. Next, simple feedforward neural networks are used as universal function approximators to map fixed-size FOFE codes to various NLP targets. This framework is appealing since it is elegant and well-founded in theory and meanwhile fairly easy and fast to train in practice. It is totally data-driven without any feature engineering, and equally applicable to a wide range of NLP tasks. In this talk, I will introduce our recent work to apply this approach to several important NLP tasks, such as word embedding [4], language modelling [1], named entity recognition (NER) and mention detection [2], coreference resolution and text categorization. Experiments have shown that the proposed approach yields strong performance in all examined tasks, including Google 1-billion-word language modelling, TAC KBP EDL contests, Pronoun Disambiguation Problem (PDP) in Winograd Schema Challenge [3]. Finally, I will briefly mention some on-going work to apply this approach to more NLP problems, such as factoid Q/A, word sense disambiguation (WSD), parsing and machine translation.